Which Dimension of Corporate Social Responsibility is a Value Driver in the Oil and Gas Industry?
Bibliographic record
Abstract
Abstract The oil and gas (O&G) industry suffers from a negative perception of poor sustainability. O&G companies are therefore engaged in several socially sustainable activities related to community development and environmental protection. This article determines whether the social, environmental, and economic dimensions of corporate social responsibility (CSR) are equally value‐additive to O&G companies. We measure the company‐specific level of CSR activities from the information provided in the annual financial reports of O&G companies and determine the effects of CSR dimensions on firm value. We find that CSR enhances firm value of O&G companies. While social activities such as employee well‐being and community development are key value‐drivers, environmental and economic sustainable activities have an insignificant impact on the market value of O&G companies. Copyright © 2018 ASAC. Published by John Wiley & Sons, Ltd
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Finance study of which corporate social responsibility dimensions drive firm value in oil and gas; the object is firm valuation.
This study analyzes corporate social responsibility and firm value in the oil and gas industry, not research practice.
Business study of CSR dimensions and firm value in oil and gas; corporate finance object.
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".